Key Features of ndCurveMaster

ndCurveMaster automatically creates multiple alternative nonlinear regression models — quickly, smartly, and without coding.

Models can include just a few predictors or advanced variable combinations, for example:

• Simple nonlinear model:
  Y = a0 + a1 · exp(x1)-0.5 + a2 · (ln(x2))8 + a3 · exp(x3)1.5 + a4 · x44.1 + a5 · x55 + a6 · x66
• Model with variable combinations:
  Y = a0 + a1 · exp(x1)-0.1 + a2 · x2 + a3 · x3 + a4 · x4(1/8) + a5 · x10.9 · x20.9 + a6 · x11.35 · x3 + a7 · (ln(x1))5 · ln(x4) + a8 · x2 · x3 + a9 · (ln(x3))8 · x40.55 + a10 · x11.25 · x2 · x3 + a11 · x2(1/2) · x36 · x40.8
• High-order polynomial model:
  Y = a0 + a1 · x1 + a2 · x2 + a3 · x3 + a4 · x1 · x2 + a5 · x1 · x3 + a6 · x2 · x3 + a7 · x1 · x2 · x3 + a8 · x12 + a9 · x22 + a10 · x32 + a11 · x13 + a12 · x23 + a13 · x33 + a14 · x12 · x2 + a15 · x1 · x22 + a16 · x12 · x3 + a17 · x1 · x32 + a18 · x2 · x32 + a19 · x22 · x3 + a20 · x12 · x2 · x3 + a21 · x1 · x22 · x3 + a22 · x1 · x2 · x32

After model generation, ndCurveMaster evaluates their quality using:

• Fit statistics: R Square, Adjusted R Square, Pearson's R, Standard Error, RRMSE, RMSE for training and test sets
• Variance analysis (ANOVA)
• t-statistics and confidence intervals
• Normality checks with Q-Q plots and tests (Shapiro-Wilk, Anderson-Darling)
  Learn more about residual normality
• Variable sensitivity analysis (when residuals are not normal)
  Learn more about sensitivity analysis
• Multicollinearity detection via VIF and Pearson Correlation Matrix
  Learn more about multicollinearity
• Overfitting detection using cross-validation
  Learn more about overfitting detection
• Visual checks of heteroscedasticity with residual plots

Model search is powered by heuristic and randomized methods.
Learn more about model search algorithms

During the search, you can:

• Set a maximum acceptable level of multicollinearity (VIF)
• Limit model overfitting
• Specify the minimum value of regression coefficients
• Lock specific predictors to keep them unchanged
• Speed up the process by restricting to basic functions, and run a full search later

ndCurveMaster uses up to 380 built-in base functions (power, exponential, logarithmic, trigonometric), organized in 5 collections, plus your own custom formulas.

Full Summary of Features

• Fully automated search and fitting of nonlinear regression models
• Evaluation of models with R Square, Adjusted R Square, RMSE, Standard Error, RRMSE
• Normality testing of residuals (Shapiro-Wilk, Anderson-Darling) and Q-Q plot
• Variable sensitivity analysis for non-normal residuals
• Detection and prevention of overfitting using cross-validation
• Multicollinearity detection via VIF and Pearson Correlation Matrix
• Polynomial regression of any order and combination of variables
• Support for unlimited number of variables and complex combinations
• Heuristic and full random search algorithms for better model discovery
• Multi-threaded processing for faster computation
• Support for weighted data
• History and ranking of models
• Option to manually expand or reduce models
• Import data from CSV, TXT, XLSX, XLS
• Access to 380+ built-in functions plus user-defined functions
• Generation of models in C/C++, Pascal and export to Python
• Copy, save, or reload results easily
• High-quality 2D plots: fitted lines, residuals, histograms
• Set your own alpha significance level
• Optimized to handle large datasets